Korea Advanced Institute of Science and Technology

Electronics and Telecommunications Research Institute

Korean Broadcasting System

University-Industry Cooperation Group of Kyung Hee University

Kwangwoon University Industry-Academic Collaboration Foundation

Silicon Works Co., Ltd.

Agency for Defense Development

Samsung Electronics Co., Ltd.

Mun Churl Kim

Bum Shik Lee

Hui Yong Kim

Jin Soo Choi

Se Yoon Jeong

Jin Ho Lee

Ha Hyun Lee

Jong Ho Kim

Suk Hee Cho

Byung Sun Kim

Keun Sik Lee

Sang Jin Hahm

Chie Teuk Ahn

Chang Seob Park

In Joon Cho

Sung Chang Lim

Jin Woong Kim

Jae Il Kim

Yong Woo Kim

Gwang Hoon Park

Jin Woo Hong

Sung-Chang Lim

Jae Seok Choi

Sea Nae Park

Hae Chul Choi

Seoung Jun Oh

Neung Joo Hwang

Dong Gyu Sim

Dae Eun Kim

Se Hwan Ki

Jae Ii Kim

Keun Woo Park

Dong Hyun Kim

Seoung-Jun Oh

Dong-Gyu Sim

Hae-Chul Choi

Jinho Lee

Jongho Kim

Sea-Nae Park

Sukhee Cho

Hahyun Lee

Seung Ho Baek

Jong Hwan Ko

Jae Gark Choi

Myoung Ho Lee

Soo Ye Kim

Jeung Won Choi

Woo Suk Ha

Neung-Joo Hwang

Ki Nam Jun

Sang Yeon Kim

Dong Hyu Sim

No Hyeok Park

Ju Hyun Jung

Disclosed decoding method of the intra prediction mode comprises the steps of: determining whether an intra prediction mode of a present prediction unit is the same as a first candidate intra prediction mode or as a second candidate intra prediction mode on the basis of 1-bit information; and determining, among said first candidate intra prediction mode and said second candidate intra prediction mode, which candidate intra prediction mode is the same as the intra prediction mode of said present prediction unit on the basis of additional 1-bit information, if the intra prediction mode of the present prediction unit is the same as at least either the first candidate intra prediction mode or the second candidate intra prediction mode, and decoding the intra prediction mode of the present prediction unit.

Method for encoding/decoding block information using quad tree, and device for using same

Disclosed are a video processing method and a device therefor. The video processing method may include receiving a video comprising a plurality of temporal portions, receiving a first model parameter corresponding to a first neural network to process the video entirely, receiving residues between the first model parameter and a plurality of second model parameters corresponding to a plurality of second neural networks to process the plurality of temporal portions, and performing at least one of super-resolution, reverse or inverse tone mapping, tone mapping, frame interpolation, motion deblurring, denoising, and compression artifact removal on the video based on the residues.

Video processing method and apparatus

Disclosed are a method of encoding a division block in video encoding and a method of decoding a division block in video decoding. An input picture is divided into encoding unit blocks. The encoding unit blocks are divided into sub-blocks. The sub-blocks are encoded by selectively using at least one of intra prediction encoding and inter prediction encoding. A decoding process is performed through a reverse process of the encoding method. When pixel values of an encoding unit block are encoded in video encoding, the flexibility in selecting an encoding mode is increased and the efficiency of encoding is increased.

Video encoding method for encoding division block, video decoding method for decoding division block, and recording medium for implementing the same

Disclosed according to one exemplary embodiment includes not limited to: a filtering unit configured to generate filtering information by filtering a residual image corresponding to a difference between an original image and a prediction image; an inverse filtering unit configured to generate inverse filtering information by inversely filtering the filtering information; an estimator configured to generate the prediction image based on the original image and reconstruction information; a CNN-based in-loop filter configured to receive the inverse filtering information and the prediction image and to output the reconstruction information; and an encoder configured to perform encoding based on the filtering information and information of the prediction image, and wherein the CNN-based in-loop filter is trained for each of the plurality of artefact sections according to an artefact value or for each of the plurality of quantization parameter sections according to a quantization parameter.

Image processing apparatuses including CNN-based in-loop filter

Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the image; and a compressor for compressing the feature map into units of at least one line; and a decompressor for reconstructing the feature map compressed into units of lines.

Image processing method and device using feature map compression

Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; at least one first line buffer for outputting the image into a line-wise image line; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the output from the first line buffer; and a feature map processor for storing the output from the first convolution operator in units of at least one line, and processing so as to output the feature map stored in units of at least one line into a two-dimensional form, wherein at least one convolution operation operates in the form of a pipeline.

Image pipeline processing method and device

Method and device of super resolution using feature map compression

Image processing method and device using a line-wise operation

In this invention, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this invention first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.

Method and apparatus for inverse tone mapping

Encoding and decoding apparatuses including CNN-based in-loop filter

Provided are a method and an apparatus for performing scalable video decoding, wherein the method and the apparatus down-sample input video, determine the down-sampled input video as base layer video, generate prediction video for enhancement layer video by applying an up-scaling filter to the base layer video, and code the base layer video and the prediction video, wherein the up-scaling filter is a convolution filter of a deep neural network.

Apparatus and method for performing scalable video decoding

An image processing method and apparatus using a line unit operation is disclosed. The image processing apparatus includes a receiver configured to receive an image, at least one first line buffer configured to output the received image to be an image line of a line unit, a first convolution operator configured to generate a feature map by performing a convolution operation based on the output image line, and a feature map processor configured to store the generated feature map as at least one line unit and process the feature map stored as the line unit to be output in a two-dimensional (2D) or higher dimensional form.

Super-resolution method and device using linewise operation

Growing community of inventors

Daejeon, South Korea

Mun Churl Kim